\chapter{Introduction}


%In a distributed healthcare environment, communication between heterogeneous systems is the biggest
%challenge for system implementers. The introduction of Electronic Health Record (EHR) envisions a
%better healthcare system by providing standards that can be used to build interoperable information
%systems for communication in the e-Health environment. A key property of an EHR system is to enable
%health professionals to exchange clinical data that are stored in different systems. EHR standards
%provide communication mechanisms that make sure the clinical information can be interpreted in a
%meaningful way at the receiving end of system [ref ehr requirement std]. 
%
%
%Recent specifications of EHR standards embark a ‘Two-level’ modelling approach to build EHR systems,
%which consist of a ‘Reference information model’ layer and a domain knowledge layer. The aspiration
%of the two-level model EHR is to separate the concepts that are of implementation detail and the
%concepts that are re-usable medical knowledge. 
%
%
%It is worth pointing out that a fully semantic interoperable EHR system requires more than the EHR
%information model to function properly. The EHR information model defines how the clinical data
%should be stored and exchanged. Clinical terminology, also known as controlled clinical
%vocabularies, are used in parallel with EHR to ensure that the clinical meaning of the information
%is conveyed correctly.

\section{Introduction and background}
\label{sec:introback}

The population of the Republic of Ireland grew from 3.44 million in 1980 to 4.5 million in 2011.
That is an increase of 1 million citizens; a very significant population growth of 31\%
\parencite{irl1980census}.  The
percentage of population over 60 - those who are most in need of healthcare, increased by 48\% in
this time. One would think that larger and older populations lead to higher demand for healthcare.
However, during that same period, the number of acute hospital beds per millions of
population in the Republic actually dropped from $5,100$ in 1980 to $3,100$ in 2000 \parencite{imo2007beds}, a drop of 39\%! 

This striking decrease of acute hospital beds is of course part of Irish government health strategy,
and has been accompanied by the closure of smaller hospitals, wards and care centres, so that care
could be focused in a small number of healthcare ``centres of excellence''. There has also been an
increase  in the number  of specialists and multi-disciplinary teams, as pointed out by a number of
reports \parencite{Schieber01081991,euro2007beds}. These
changes are evidence that the Irish healthcare system, like healthcare systems across the world, is
undergoing a transition from many generic, relatively non-specialised hospitals, to a small number
of highly expert centres, supported by widely distributed, community based 
healthcare \parencite{wright1993community, magnussen2007centralized}. The watchword
for this new system is ``shared care'' and it depends on the safe, effective and timely transfer of
patients and information between cooperating healthcare provider organisations and professionals.

It has been noted that shared information flows must accompany patient referrals in a shared care
environment. In order for the shared care process to function between organisations, there must be
secure and high quality healthcare data exchange. So that when patients transfer from one hospital
to another, one department to another and one country to another, the accumulated clinical data can
be re-used in the patient's new circumstances. If health professionals in a shared care community
cannot receive high quality information from their peers, clinical diagnosis and treatments will be based
on previous stored patient health
information or new investigations. It is as if such information does not exist. As a result, many
tests clinical investigations and clinical judgements are repeated for a lot
of patients. This is just one of the problems that results from poor communication of health
information. Clearly the linking and sharing of clinical information from heterogeneous clinical
information systems between large healthcare providers, hospitals, GPs is essential for increasing
healthcare quality and efficiency. Central to this process of linking scattered information is the
adoption of,
\begin{enumerate}
  \item Shared and agreed clinical terminology 
  \item Commonly understood health information models 
\end{enumerate}






%This work focuses on a mechanism to facilitate comparison between these two extremely important
%contributors to the quality of shared health information. 





%Technological advances in healthcare have changed many ways that physicians treat patients in
%hospitals and other healthcare organisations. Doctors and healthcare professionals are expected to 
%work collaboratively even across organisation boundaries to deliver patient care. 

%This means… Traditionally sharing information …

%Clinical information systems in the healthcare industry play an important role in supporting clinicians
%to deliver high quality health service and to ensure the safety of patients. Examples of clinical
%information systems include patient administration systems, laboratory information systems, radiology
%systems, hospital departmental information systems and so on, depending on different clinical purposes. Each of these categories
%covers software systems from numerous commercial vendors that provide functionalities to support
%daily healthcare activities. For example, an information system for
%general practitioners (GP) can be used to store patient notes, results of medical
%consultation and laboratory tests, medical history and other information electronically. 
%Oncology information systems store and manage cancer patients' information such as diagnostic
%information, treatments, and clinical outcomes. The deployment of these information systems in
%hospitals and healthcare sites helps to improve the management of clinical information and achieve a
%paper-free healthcare environment. More importantly, these systems are designed to ensure patient
%safety by improving the efficiency of the careflow and reducing human errors. 

Clearly, given the drive towards ``shared care'', information sharing has been a key requirement in modern healthcare services. Healthcare is
delivered to patients by health professionals from various sites that form a network of care givers
with the patient in the centre. The distributed careflow that a patient undertakes creates a large
amount of health information that is stored in different information systems. For instance when the
patient visits a GP, the medical history is recorded in the GP system. When the patient has been
referred to see a specialist, the encounter will be recorded in a patient management system. When a
laboratory test has been ordered, a laboratory system manages the sample information and the results. However,
connecting these medical activities to provide a seamless careflow requires these diverse clinical
systems to communicate with each other. The medical consultant should be able to view the laboratory
reports, while the GP system in this example should get information about the consultation electronically.
Although a few integrated solutions from commercial information system providers have the
capability to allow communication between different systems, it still remains a difficult task to
integrate the large number of heterogeneous clinical systems. Ad hoc or single vendor communication approaches 
can provide a limited solution to information sharing. In the long
run, however, the cost and scalability issue will hamper the efficiency of delivering high standard
healthcare. Several expensive national projects, such as the The National Programme for IT in the
United Kingdom (NPfIT) \parencite{npfit-nhs2007}, had been aiming to implement a nation wide integrated health
information system, however proved to be unfruitful \parencite{npfit-out2011, npfit-out2013}.

% Damons request to add a strong start
%Patients can only get benefit from sharing health information if secure and high quality healthcare data exchange is in
%place. So that when patients transfer from one hospital to another, one department to another and
%one country to another, the accumulated clinical data can be re-used. In most hospitals, clinical
%diagnosis and treatments are not benefiting from previously stored patient health information.
%Therefore many tests and clinical investigation are repeated for a lot of patients. Linking and
%sharing clinical information from heterogeneous clinical information systems 
%between large healthcare providers, hospitals, GPs is essential for
%increasing healthcare quality and efficiency.
%


A new approach to tackle the integration issues between different clinical
systems, which are the data sources of patient information, involves creating a patient centred record
that contains all possible health information about that patient. In this view, all clinical data are part of the
patient's life-long careflow. Individual healthcare professionals only need to interact with a
portion of the record. The record system also keeps audit trails on every piece of information
to provide provenance for patient safety.   
Over the years the term Electronic Health Record (EHR)
has been used to describe the idea of a patient centred record that integrates information in a
distributed healthcare environment. 
The ISO technical report 20514 ``Health informatics --
Electronic health record -- Definition, scope and context'' \parencite{iso/tr20514} 
provides a definition of a shared care
record as a \emph{Integrated Care Electronic Health Record} (ICEHR). The ISO document also provides
a definition of an Electronic Health Record system.
The two pillars of an integrated health record are:
\begin{enumerate}
  \item An information model that covers as wide as possible every aspect of healthcare
  \item Clinical terminologies that every healthcare professional can understand and agree
  \end{enumerate}
A number of Electronic Health Record standards have been published to provide the information models
that aim to cover the space of all healthcare activities. In the meantime medical terminologies are
continually being developed to provide standardised vocabularies. How can these two components be
used to solve the interoperability issues among clinical systems? 




\section{EHR and medical terminologies }
\label{sec:tech-advances}
The problem with the modern information systems that are deployed in various healthcare sites is
that exchanging information becomes increasingly difficult as the number of heterogeneous
information systems increases. If not relying on a paper based method, communication is often allowed
between a limited number of systems to share resources. Each system may have to create an adaptation
layer to exchange information with each other individual system in the community. 
There are many downsides to this approach. This solution is not scalable since communication between
$n$ different hospitals requires the order of $n^2$ transformations of information. Despite
scalability issues, this may introduce a security risk to many crucial operational systems in
a hospital. Large numbers of adaptation layers can hardly satisfy the growing requirements of routine
healthcare activities and clinical research.  For example, after a patient has completed a surgical
procedure to remove or extract a sample for inspection, a pathology report will be generated and
stored in a laboratory information system, while the patient outcome information is stored in a
separate system. The clinician who is in charge of the patient would need to have access to both
systems to combine the information to support clinical decisions or diagnosis. Solving the problem
of  interoperability between clinical information systems has been the focus of developing a future
proof e-health ecosystem.


\subsection{Electronic Health Record}
The advocates of Electronic Health Record envision a better healthcare paradigm by providing an
integrated healthcare record that stores all information that could be generated during the
careflow. Information systems that are built around the Electronic Health Record could potentially
solve many problems in a distributed e-health environment. A key property of an EHR system is to
enable healthcare professionals to exchange clinical data that are stored in different systems.
However, having an EHR does not guarantee that the clinical information that is being
exchanged will be interpreted meaningfully across systems. This is because while an EHR provides a
structure to store clinical information, it does not always necessarily express the semantics of
information in a standardised way. The use of clinical terminologies
provides a unified way of expressing unambiguous clinical concepts via standardised medical
vocabularies.


The patient, who undergoes a complex careflow in a hospital or a number of healthcare sites,
inevitably generates large volumes of clinical data in a multi-dimensional space that have dispersed
into various heterogeneous systems. In order to re-group the clinical information to form the whole
picture of the complete patient healthcare information, a new approach has been inspired to give a
patient centred view of all the clinical data. The Electronic Health Record based
approach is intended to gather and organise all clinical data that are associated with the patient from cradle to
grave \parencite{grimson2001delivering}. 
There had been many systems built to implement a full EHR. The Veterans Health Information
Systems and Technology Architecture (VistA) 
was perhaps the largest and most comprehensive
information system that has ever built around a core electronic health record \parencite{brown2003vista,
bouhaddou2008va}. In the last
two decades, a number of international industrial organisations and initiatives began to standardise
the implementation of an EHR system and produced a set of specifications that formally defines the roles
and features of an EHR system. These standards provide the blueprint of the clinical data that can
be stored in an EHR -- a common information model that represents the structure and content of
healthcare information that would be associated with a patient's careflow.


\subsection{Clinical terminologies}
As with any communication method, a common vocabulary is required between clinical systems to be
understood by both communicating parties. Clinical terminologies contain large sets of designated
codes to represent clinical statements or concepts. Examples include diagnosis codes such as the
International Statistical Classification of Diseases (ICD) \parencite{icd10manual}
which are widely used in hospitals. The use of clinical terminologies helps to deliver clear and
precise  clinical meanings during any communication instead of any vague description.

Over many years of development across various medical domains, modern clinical terminologies have
gradually incorporated ``ontologies'' into the various codes and vocabularies become more
sophisticated than simple text and definition. These clinical terminologies are sometimes referred
to as ``medical ontologies''. Like all ontologies they consist of codes and descriptions with 
hierarchies and relationships between coded terms  to model real world medical phenomena. 
Medical ontologies will be further discussed in chapter 2. 


\section{The problem}
\label{sec:the-problem}
Most clinical systems have an internal representation of the clinical information stored. In
computer science and software engineering it is commonly known as the core Information Model of the
system. When exchange of information is required, conflicts are likely to happen due to the
different representations of data in different systems.  

The purpose of an integrated EHR is to create a most comprehensive information model that covers every
possible aspect of healthcare with which heterogeneous clinical systems could be integrated.
Healthcare is a complex and information rich domain, consequently the resulting model will inevitably be large. 
A number of EHR standards feature 
large and complex information models to cover the relevant medical domains.

There are a number of issues to address in order to build a fully functioning EHR system, beside having to
continue developing the core model to accommodate all clinical needs. Other components including
patient identification management, low level infrastructure, security, confidentiality and access
control are all crucial parts of an EHR system that connects to other vital systems in a hospital.
However two components are largely focused and particularly relevant in this work, which are 
\begin{enumerate}
  \item the mechanism to develop and extend an EHR information model
  \item the mechanism to link an information model with clinical terminologies
\end{enumerate}


The holy grail of clinical system integration, which is also part of the mission of implementing an
integrated EHR, is to merge the internal models that represent heterogeneous clinical content in
different systems. This is considered a very difficult task because of the diversity of clinical data,
multiple representations of the same clinical information, and the unstructured data that the
semantics can only be understood by human (at certain sites). Therefore the capability to
continually extend and modify the EHR information model is an essential feature for a successful EHR
model. This inspires what is called a ``two-level'' EHR model that contains an expandable meta data
layer. A typical mechanism that has been introduced into several EHR standards to achieve the
expansion is via defining a meta data resource called ``Archetypes''. Additional description of this type
of meta data will be provided in later chapters.
A growing information model of this type requires significant time and resources to develop the meta data
resource, which supports the representation of clinical content in an EHR. 

Similarly, clinical terminology development requires large amount of effort to model carefully coded
clinical entities that could be used in a wide medical area. To achieve higher level of
understanding during communication, both EHR information model and clinical terminologies are needed
for sharing clinical information between systems in a meaningful way. 

The challenge here is, how to seamlessly integrate the EHR information model with clinical terminologies
seamlessly?

There are many ways of expressing the same clinical meaning in the medical domain. For example the
event of death could be recorded as an event in a patient's history, but it could also be stored in
another system with a coded attribute `Deceased'. Differences in representation of this type occur
as a result of the lack of a
canonical form for recording clinical information. With the slow but growing development of both EHR
meta data and clinical terminologies, it seems to require a lot of effort to integrate the two to
represent clinical content correctly. How to best link these semantic resources to support
healthcare services? How to improve the development process of both resources?
This thesis attempts to answer these questions by exploring the problem of integrating EHR information
models with clinical terminologies.

%Real world problems such as \ldots people keep dev IM archetypes, ontology .. inevitably more
%integration between 2 needed.. need a study to start investigating..


\section{Aims and objectives}
The core research questions that this thesis aims to explore are:
\begin{enumerate}
  \item How do clinical terminologies and clinical information models impact on each other?
  \item How to harmonise the development of clinical information models and clinical terminologies to
facilitate semantic interoperability in a fully integrated EHR?
\end{enumerate}

Focusing on these questions, the following objectives have been set up as the backbone of the
studies in this thesis:
 
\emph{Objective 1:} Investigate the two different ways of expressing health information, a) by
clinical
information model meta data resource such as Archetypes, b) encoded with
controlled vocabulary / medical ontology;
 
\emph{Objective 2:} Investigate and explore the linkages that have been created between the two
representations of clinical information.
 
\emph{Objective 3:} Discover how to utilise the linkage mechanism and associated processes to
promote better integration between clinical information model meta data resource and clinical
terminologies.
 
\emph{Objective 4:} To propose a viable mediating resource between EHR information models and
clinical terminologies and a platform that supports semi-automatic creation of linkages 
between clinical information models and terminologies.

\emph{Objective 5:} Investigate the eligibility,  feasibility and performance of the mediating
resource and the platform.
 
\emph{Objective 6:} Demonstrate the benefits of harmonising clinical information models with
clinical terminologies towards the development of both semantic materials.
 




\section{Intended contribution of this work}
The overall contribution of this work will be the \emph{introduction and implementation of a novel
mediating resource between EHR information models and terminologies, and a framework that
facilitates a detailed comparison of elements of information models and with corresponding parts of
a terminological system.}

This will be supported by the following more specific contributions resulting from the application of
the proposed approach to the healthcare domain.

\textbf{C.1} \emph{This work will explore the state of art in technologies and approaches for using clinical
terminologies to enhance the effectiveness of contemporary clinical information models for an
integrated care EHR.}
 
\textbf{C.2} \emph{This work will introduce a new approach for instantiating an EHR-terminology mediating resource
using the relationship between models of clinical information called archetypes on one hand and
SNOMED-CT, the most substantial clinical terminological system on the other. }

\textbf{C.3a} \emph{The development of a framework to create and evaluate the efficiency of the resource in terms
of integrating EHR information model meta-data with clinical terminologies will be described.}


\textbf{C.3b} \emph{The thesis will describe the development of an approach to (semi-)automatically create the
mediating resource using this framework.}


\textbf{C.4} \emph{Using the framework, this work will evaluate the effectiveness of this new approach that uses
the mediating resource, by performing assessment on both the viability and the performance of the
resource.}


\textbf{C.5} \emph{The author will demonstrate the applicability of this mediating resource, by providing example
applications, including a use case that calculates the coverage of clinical concepts in an archetype
repository with respect to a major clinical terminology. The thesis will demonstrate the
applicability of this mediating resource both in healthcare and in other domains and consequently the potential
impact of this approach.}





\section{Structure of the thesis}
The rest of the thesis is organised as follows:

Chapter 2: \emph{Literature review: Introducing EHR and clinical terminologies} 
is the first part of the literature review. It describes, with references
to the literature, the background to this work and the challenge of
achieving communication between heterogeneous systems in a distributed healthcare environment.
It  introduces the EHR standards that endorse a `two-level' modelling approach to build EHR
systems, which consist of a `base information model' layer and a domain knowledge layer. The chapter
introduces the purpose of designing a two-level model based EHR which is to separate the concepts
that are details of implementation and the concepts that are re-usable medical knowledge. 
It also describes a brief history of clinical terminologies, which are used in parallel with EHR to
ensure the correct interpretation of clinical meanings.

Chapter 3: \emph{Literature review: State of the art of integrating  EHRs with terminologies} continues the literature
review. It introduces a selection of relevant work that has been done in the area to map
texts to concepts,  terminology services, and other medical text processing that can be used to
associate free text with clinical terminologies. Also it surveys similar work that has been done to
harmonise EHR meta data with clinical terminologies.

Chapter 4: \emph{The Terminological Shadow} introduces a mediating resource called a `terminological
shadow' that was developed by the author in this work, which aims to preserve the EHR meta data
information and the corresponding clinical concepts in terminologies. The chapter also describes a
framework to construct and evaluate shadows. The final part of the chapter describes the plans for
evaluating shadows and proving the applicability of shadows.

Chapter 5: \emph{Evaluating terminological shadows} describes a two-step evaluation process that aims to
assess the effectiveness of the shadow construction method and the quality of the resulting shadows.
It shows some quantitative results of the evaluation and also discusses the results of evaluation to
reveal the relationship between EHR meta data and clinical terminologies by using some standard
measures from the Information Retrieval research domain.

Chapter 6: \emph{Applications of terminological shadows} presents two examples of utilising terminological
shadows to demonstrate the applicability of the mediating resource. The first example uses shadows
to show how many clinical concepts have been covered by the EHR meta data that have been developed
by a community of clinical informatics. The second application demonstrates the use of shadows to
measure similarity between the EHR meta data by their semantic meanings.

Chapter 7: \emph{Summary and conclusion} revisit the research aims and the contributions to summarise the
work as a whole. The chapter also discusses possible extensions of the study as future work. 







